Computational Intelligence in Traffic Sign Recognition
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- 1.2 Difficulties in detecting and recognizing traffic signs
1.1 MotivationIn 1968 the Europe countries signed an international treaty, called the Vienna convention on road traffic, for the basic traffic rules. More information about the treaty and traffic signs in The Netherlands can be found in Appendix 1. The aim of standardizing traffic regulations in participating countries in order to facilitate international road traffic and to increase road safety. A part of this treaty defined the traffic signs and signals, which results in well standardized traffic signs in Europe. Language differences can create difficulties in understanding the traffic signs, therefore are symbols used, instead of words, during the development of the international traffic signs. It is expected that the introduction of the treaty results in traffic signs that can be easily recognized by human drivers. However, according to a recent survey conducted by a motoring website2, one in three motorists fail to recognize even the most basic traffic signs. Al-Madani & Al-Janahi [3] also concluded in their study that only 56 percent of the drivers recognized the traffic signs. In other words, the traffic signs are not that easily recognized by human drivers as we first thought3. To conclude, a TSDR system that assist the driver can significantly increase driving safety and comfort. There are also other applications for a system that can detect and recognize traffic signs. For instance, a highway maintenance system that can verify the presence and conditions of traffic signs. Further more, it can be used in intelligent autonomous vehicles. They can function in far greater scope of locations and conditions than manned vehicles. 1.2 Difficulties in detecting and recognizing traffic signsAt first sight the objective of TSDR is well defined and seems to be quite simple. Lets consider a camera that is mounted into a car. This camera captures a stream of images and the system detects and recognizes the traffic signs in the retrieved images. For a graphical view see Figure 3. Unfortunately there are, besides the positive aspects, also some negative aspects. The positive aspects of TSDR is the uniqueness of the design of traffic signs, colours contrast usually very well against the environment, the signs are strictly positioned relative to the environment and are often set up in a clear sight to the driver. On the other hand, there are still a number of negative aspects of TSDR. We can distinguish the following aspects: Lightning conditions are changeable and not controllable. Lightning is different according to the time of the day and season, weather conditions and local light variations such as direction of light (Figure 4 and Figure 6). The presence of other objects like pedestrians, trees, other vehicles, billboards, and buildings. This can cause partial occlusion and shadows. The objects or surrounding could be similar to traffic signs by colour or shape (Figure 5 and Figure 8). The sign installation and surface material can physically change over time, influenced by accidents and weather, thus resulting in disoriented and damaged signs and degenerated colours (Figure 7). The retrieved images from the camera of a moving car often suffers from motion blur and car vibration. It is not possible to generate an offline model of all the possible appearances of the sign, because there are so many degrees of freedom. The object size depends on the distance to the camera. Further more, the camera is not always perpendicular to the signs, which produces an aspect modification. The detection and recognition of traffic signs are caught up with the performance of a system in real-time. This requires a system with efficient algorithms and powerful hardware. Traffic signs exists in hundreds of variants often different from legally defined standards. 
Figure 3 Simple overview of the traffic sign recognition system Thus, to construct a successful TSDR system one must provide a large number of traffic sign examples to make the system respond correctly to real traffic images. This requires large databases what is expensive and a time consuming task. 
Figure 4 Local lightning can make it difficult to recognize traffic signs. 
Figure 5 Hard to recognize the blue traffic sign with the blue sky. 
Figure 6 Bad weather conditions. 
Figure 7 Damaged traffic signs. 
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